Impact of Network Structure on Malware Propagation: A Growth Curve Perspective

Guo, Hong; Cheng, Hsing Kenneth; Kelley, Ken

doi:10.1080/07421222.2016.1172440

Cited by 20 publications

(9 citation statements)

References 58 publications

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“…ln (v) nµL. (11) Obviously, C (K/n ) is always greater than zero. At this time, C (0) < 0 can be expressed as…”

Section: Optimal Allocation Of Cybersecurity Investmentmentioning

confidence: 99%

“…Interdependent firms and branches interconnect their databases or share information to improve the efficiency of information exchanges [8,9], which in turn would expose the participating firms or branches to security risks. On the one hand, participating firms or branches physically connect to each other through a mutual trust network or interface, and the other information systems will become vulnerable if any of them are compromised by hackers, which makes it easier for hackers to penetrate their systems via mutual trusted IT infrastructure, e.g., local area networks (LANs) and wide area networks (WANs), and steal sensitive data [10,11]. For example, Cainiao, the logistic arm of Alibaba, run the largest logistics database all over the world by establishing a platform integrating information resources of third-party logistics systems [12].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies mainly focused on optimizing security investment in terms of system interconnection and information sharing among homogeneous firms [5,11,17]. Considering the fact that there are limited budgets on security investment which are often at a disadvantage when competing with that on other investment projects [18], one must allocate optimally the limited budgets to all branches including headquarters to achieve the highest cybersecurity gains, which, as authors known, is rarely mentioned in previous studies.…”

Section: Introductionmentioning

confidence: 99%

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Cybersecurity Investment Allocation for a Multi-Branch Firm: Modeling and Optimization

2019

Mathematics

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Network interconnection and information sharing among firms and their departments expose them to cybersecurity breaches. Traditional cybersecurity studies have paid little attention to the reallocation of security investment within firms. This paper proposes a mathematical model for optimal allocation of cybersecurity investment among headquarters and branches with budget constraints. The differences in size of information sets and system interconnection have been taken into account. The responses of optimal allocation to internal and external factors, such as the portion of branch information set, the propagation probability, the budget constraints, and the intrinsic vulnerability, have been studied in deep both theoretically and numerically. Analysis results indicate that the group will give priority to protecting headquarters when the total budget is small and intrinsic vulnerability is high. The security investment allocated to each branch increases with budget, propagation probability and portion of information set, but never exceeds 1 / ( n + 1 ) of total budget. Numerical simulations also verify that security information sharing among headquarters and branches can help improve the efficiency of security investment in the whole system. Furthermore, the findings of this paper will draw attention to the reallocation of cybersecurity investment within a business group and help cybersecurity managers to develop investment allocation strategies and policies.

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“…ln (v) nµL. (11) Obviously, C (K/n ) is always greater than zero. At this time, C (0) < 0 can be expressed as…”

Section: Optimal Allocation Of Cybersecurity Investmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cybersecurity Investment Allocation for a Multi-Branch Firm: Modeling and Optimization

2019

Mathematics

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“…Menggambarkan jaringan teknologi yang dihasilkan, diwakili oleh grafik yang tidak diarahkan. [12] Gambar 2. Technological Network Setiap paket berpindah dari sumber ke tujuan dengan dua protokol berbeda yang menggunakan port TCP dan UDP dan kedua protokol tersebut memiliki hingga 65.536 port yang berbeda.…”

Section: Hasil Dan Pembahasan a Gambaran Umumunclassified

Malware Security Menggunakan Filtering Firewall Dengan Metode Port Blocking Pada Mikrotik RB 1100AHx2

Ryansyah

Maulana²

2018

justin

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Penelitian ini bertujuan untuk mengatasi permasalahan penyebaran malware yang terdapat dalam jaringan kampus. Salah satu dampak adanya malware dalam jaringan kampus adalah overload traffic bandwidth, sehingga menyebabkan kendala bandwidth yang cepat habis atau lalu lintas transfer data baik yang masuk maupun yang keluar menjadi lambat dari biasanya. Umumnya sebuah kampus atau universitas memiliki struktur jaringan yang didalamnya dikelola oleh satu atau lebih router di dalam mengelola jaringan dan bandwidth. Beberapa router memiliki kemampuan pengaturan firewall yang sudah cukum mumpuni namun perlu dikelola lebih spsesifik berdasarkan kebutuhan skala jaringan dan bandwidth yang tersedia. Dengan menciptakan rule-rule yang baik di dalam firewall akan lebih mudah dalam melakukan filtering terhadap lalu lintas trafik jaringan dan bandwidth sehingga dapat menciptakan keamanan dan kenyamanan pengguna jaringan dan bandwidth.

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“…The logical centralization of network intelligence presents exciting challenges and opportunities to enhance security in such networks, including new ways to prevent, detect, and react to threats, as well as innovative security services and applications that are built upon SDN capabilities. Malicious code detection and prevention under the new architecture need further study [7][8][9][10][11][12][13][14].…”

Section: Research Trends and Gapsmentioning

confidence: 99%

Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

Liu

Ren

et al. 2017

Security and Communication Networks

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As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q<qc. The results showed that our model is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

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Impact of Network Structure on Malware Propagation: A Growth Curve Perspective

Cited by 20 publications

References 58 publications

Cybersecurity Investment Allocation for a Multi-Branch Firm: Modeling and Optimization

Cybersecurity Investment Allocation for a Multi-Branch Firm: Modeling and Optimization

Malware Security Menggunakan Filtering Firewall Dengan Metode Port Blocking Pada Mikrotik RB 1100AHx2

Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

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